Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:39:57.303439
Analysis finished2020-08-25 00:40:18.049192
Duration20.75 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz5 is highly correlated with oz3High correlation
oz3 is highly correlated with oz5High correlation
oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.18807166069746e-10
Minimum-2.284515857696533
Maximum2.3085923194885254
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:18.097007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.284515858
5-th percentile-1.663693786
Q1-0.7382829487
median-0.00907472102
Q30.80151622
95-th percentile1.605040383
Maximum2.308592319
Range4.593108177
Interquartile range (IQR)1.539799169

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-1221288777
Kurtosis-0.7292158685
Mean-8.188071661e-10
Median Absolute Deviation (MAD)0.7741458118
Skewness-0.04554015039
Sum-8.188071661e-07
Variance1.000000004
2020-08-25T00:40:18.198486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-2.22655606310.1%
 
1.33064460810.1%
 
-0.539782166510.1%
 
-1.34518623410.1%
 
0.954360067810.1%
 
-0.193085476810.1%
 
0.684911131910.1%
 
0.942115068410.1%
 
-0.114834450210.1%
 
1.6615643510.1%
 
-0.0558300353610.1%
 
1.66937005510.1%
 
-0.578823804910.1%
 
-0.981165528310.1%
 
0.927037477510.1%
 
2.00504279110.1%
 
0.717463493310.1%
 
0.510454177910.1%
 
-2.11993241310.1%
 
0.447607964310.1%
 
-0.0233977604710.1%
 
-2.00508761410.1%
 
0.255222976210.1%
 
-1.55213880510.1%
 
0.884064376410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.28451585810.1%
 
-2.22655606310.1%
 
-2.19065976110.1%
 
-2.18007373810.1%
 
-2.17908310910.1%
 
-2.14791321810.1%
 
-2.12832617810.1%
 
-2.11993241310.1%
 
-2.11487603210.1%
 
-2.11237621310.1%
 
ValueCountFrequency (%) 
2.30859231910.1%
 
2.2819254410.1%
 
2.25870561610.1%
 
2.25439858410.1%
 
2.15627646410.1%
 
2.14393258110.1%
 
2.128714810.1%
 
2.12348985710.1%
 
2.10817813910.1%
 
2.10463380810.1%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6230624169111252e-09
Minimum-1.7088311910629272
Maximum1.7083951234817505
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:18.306774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.708831191
5-th percentile-1.539201885
Q1-0.8919605762
median0.01244333014
Q30.8712132722
95-th percentile1.528078485
Maximum1.708395123
Range3.417226315
Interquartile range (IQR)1.763173848

Descriptive statistics

Standard deviation0.9999999999
Coefficient of variation (CV)-616119250.6
Kurtosis-1.252232788
Mean-1.623062417e-09
Median Absolute Deviation (MAD)0.881252408
Skewness-0.01865601364
Sum-1.623062417e-06
Variance0.9999999998
2020-08-25T00:40:18.410091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.572264671310.1%
 
-0.475824236910.1%
 
0.0380447842210.1%
 
1.45834696310.1%
 
-0.0887050256110.1%
 
1.45052766810.1%
 
-1.50520908810.1%
 
-1.55989301210.1%
 
-0.461080461710.1%
 
-0.181313484910.1%
 
-1.53644299510.1%
 
1.46612453510.1%
 
1.67443764210.1%
 
1.47002768510.1%
 
-0.83852714310.1%
 
1.3879866610.1%
 
-1.19651615610.1%
 
1.03655374110.1%
 
0.795550584810.1%
 
-0.929450094710.1%
 
-1.58420193210.1%
 
0.813124120210.1%
 
-0.721323490110.1%
 
-1.36715078410.1%
 
-1.14576685410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.70883119110.1%
 
-1.70637369210.1%
 
-1.70234894810.1%
 
-1.69945704910.1%
 
-1.69921684310.1%
 
-1.6935290110.1%
 
-1.68906712510.1%
 
-1.68138337110.1%
 
-1.68045711510.1%
 
-1.67669928110.1%
 
ValueCountFrequency (%) 
1.70839512310.1%
 
1.70277154410.1%
 
1.70166182510.1%
 
1.70150017710.1%
 
1.69400441610.1%
 
1.68946957610.1%
 
1.68688929110.1%
 
1.6843717110.1%
 
1.680712710.1%
 
1.67462277410.1%
 

oz3
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.899257767945528e-09
Minimum-2.0032403469085693
Maximum3.6457266807556152
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:18.522342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.003240347
5-th percentile-1.29123863
Q1-0.7472878993
median-0.2168897837
Q30.5654576123
95-th percentile2.006938982
Maximum3.645726681
Range5.648967028
Interquartile range (IQR)1.312745512

Descriptive statistics

Standard deviation0.9999999994
Coefficient of variation (CV)526521473.9
Kurtosis0.1747327968
Mean1.899257768e-09
Median Absolute Deviation (MAD)0.60821639
Skewness0.8119475537
Sum1.899257768e-06
Variance0.9999999987
2020-08-25T00:40:18.625640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.0494381524610.1%
 
-0.379215985510.1%
 
-0.908848822110.1%
 
0.891934335210.1%
 
-1.05206549210.1%
 
-0.292310625310.1%
 
-0.42030450710.1%
 
0.398121833810.1%
 
0.269846856610.1%
 
-0.0769828483510.1%
 
2.40530061710.1%
 
-1.14813971510.1%
 
0.158359214710.1%
 
0.200279399810.1%
 
0.0217090789210.1%
 
-0.281559407710.1%
 
-0.121950842410.1%
 
-0.94664931310.1%
 
-1.32153987910.1%
 
-0.865847289610.1%
 
0.396972566810.1%
 
0.85998529210.1%
 
1.54027581210.1%
 
0.738887786910.1%
 
-0.419247567710.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.00324034710.1%
 
-1.9132679710.1%
 
-1.80718994110.1%
 
-1.80202221910.1%
 
-1.7606679210.1%
 
-1.67592227510.1%
 
-1.67581784710.1%
 
-1.65837955510.1%
 
-1.65791547310.1%
 
-1.6333838710.1%
 
ValueCountFrequency (%) 
3.64572668110.1%
 
3.57705688510.1%
 
2.91393494610.1%
 
2.89313697810.1%
 
2.81411910110.1%
 
2.76174688310.1%
 
2.65918421710.1%
 
2.64631652810.1%
 
2.62507295610.1%
 
2.61689686810.1%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.810281097888946e-10
Minimum-1.6643346548080444
Maximum1.7528222799301147
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:18.734869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.664334655
5-th percentile-1.51190573
Q1-0.8665590584
median-0.009994781809
Q30.8806318194
95-th percentile1.567009461
Maximum1.75282228
Range3.417156935
Interquartile range (IQR)1.747190878

Descriptive statistics

Standard deviation0.9999999997
Coefficient of variation (CV)2078880588
Kurtosis-1.246763799
Mean4.810281098e-10
Median Absolute Deviation (MAD)0.8850972124
Skewness0.04279703702
Sum4.810281098e-07
Variance0.9999999994
2020-08-25T00:40:18.839070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.644530773210.1%
 
1.34892356410.1%
 
-0.334313780110.1%
 
-0.472007870710.1%
 
-0.286460816910.1%
 
-1.19271087610.1%
 
1.13020861110.1%
 
-0.541666150110.1%
 
0.137369573110.1%
 
-1.61848795410.1%
 
0.457590907810.1%
 
-0.238608270910.1%
 
-1.2512874610.1%
 
0.709623038810.1%
 
-0.750635325910.1%
 
-1.31767225310.1%
 
0.0182070881110.1%
 
-0.0930965915310.1%
 
-0.369455456710.1%
 
0.310861527910.1%
 
-1.56765425210.1%
 
1.69655740310.1%
 
-0.756480574610.1%
 
-1.09889316610.1%
 
0.926372408910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.66433465510.1%
 
-1.65781164210.1%
 
-1.65210318610.1%
 
-1.6504863510.1%
 
-1.65038251910.1%
 
-1.64897811410.1%
 
-1.64704024810.1%
 
-1.6447694310.1%
 
-1.64399254310.1%
 
-1.64293730310.1%
 
ValueCountFrequency (%) 
1.7528222810.1%
 
1.75075519110.1%
 
1.74814450710.1%
 
1.74665057710.1%
 
1.74039375810.1%
 
1.73598837910.1%
 
1.73526084410.1%
 
1.73495662210.1%
 
1.73492729710.1%
 
1.72981107210.1%
 

oz5
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.0884908735752106e-10
Minimum-1.658408522605896
Maximum4.400187969207764
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:18.952526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.658408523
5-th percentile-1.0152794
Q1-0.7016626
median-0.3280272335
Q30.4581246376
95-th percentile2.117820382
Maximum4.400187969
Range6.058596492
Interquartile range (IQR)1.159787238

Descriptive statistics

Standard deviation0.9999999976
Coefficient of variation (CV)-4788146361
Kurtosis1.905301712
Mean-2.088490874e-10
Median Absolute Deviation (MAD)0.4709094986
Skewness1.413000363
Sum-2.088490874e-07
Variance0.9999999952
2020-08-25T00:40:19.061576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.2343645110.1%
 
0.101885750910.1%
 
-0.636420011510.1%
 
1.45386195210.1%
 
-0.252278685610.1%
 
-0.633462846310.1%
 
-1.11848473510.1%
 
-0.795569300710.1%
 
-0.639317333710.1%
 
-0.278641879610.1%
 
-0.771804273110.1%
 
1.04424893910.1%
 
-0.731107354210.1%
 
1.55986726310.1%
 
0.220544770410.1%
 
-0.596333444110.1%
 
-0.608018100310.1%
 
-0.031533304610.1%
 
-0.504563331610.1%
 
1.70046877910.1%
 
-0.984998941410.1%
 
0.531532406810.1%
 
-0.729137957110.1%
 
-0.656319737410.1%
 
0.886948823910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.65840852310.1%
 
-1.52227175210.1%
 
-1.41197943710.1%
 
-1.40919697310.1%
 
-1.39554238310.1%
 
-1.37083077410.1%
 
-1.35846471810.1%
 
-1.33943414710.1%
 
-1.32030737410.1%
 
-1.29226195810.1%
 
ValueCountFrequency (%) 
4.40018796910.1%
 
4.23640298810.1%
 
4.1755580910.1%
 
4.11800003110.1%
 
4.03174161910.1%
 
3.71578717210.1%
 
3.64380240410.1%
 
3.43058347710.1%
 
3.40723180810.1%
 
3.31271433810.1%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.608890205621719e-10
Minimum-2.0312960147857666
Maximum3.8746063709259033
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:19.175287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.031296015
5-th percentile-1.329411221
Q1-0.7673729807
median-0.1580263749
Q30.5892129242
95-th percentile1.914537007
Maximum3.874606371
Range5.905902386
Interquartile range (IQR)1.356585905

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-1782883894
Kurtosis0.1737913041
Mean-5.608890206e-10
Median Absolute Deviation (MAD)0.6567605436
Skewness0.7497638725
Sum-5.608890206e-07
Variance1.000000002
2020-08-25T00:40:19.286067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.96484041210.1%
 
0.112648911810.1%
 
-1.01898646410.1%
 
0.729170322410.1%
 
0.0431316271410.1%
 
-0.809242427310.1%
 
-0.105885639810.1%
 
0.842033326610.1%
 
0.421220660210.1%
 
0.290360242110.1%
 
-0.736969113310.1%
 
2.02790331810.1%
 
1.22393190910.1%
 
0.0951352715510.1%
 
1.03388643310.1%
 
-0.172033280110.1%
 
-0.213039934610.1%
 
-0.481129646310.1%
 
-0.966168940110.1%
 
1.63406407810.1%
 
-0.779920041610.1%
 
-0.824841082110.1%
 
-0.306950569210.1%
 
-0.165682122110.1%
 
-0.958791077110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.03129601510.1%
 
-1.84818065210.1%
 
-1.72553515410.1%
 
-1.71218347510.1%
 
-1.7120094310.1%
 
-1.69528210210.1%
 
-1.68655645810.1%
 
-1.64330995110.1%
 
-1.63040685710.1%
 
-1.60603034510.1%
 
ValueCountFrequency (%) 
3.87460637110.1%
 
3.44685339910.1%
 
3.41228747410.1%
 
3.24671053910.1%
 
3.17336630810.1%
 
2.93690705310.1%
 
2.93569040310.1%
 
2.79023313510.1%
 
2.68124389610.1%
 
2.65941166910.1%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.8044374883174896e-11
Minimum-1.7106292247772217
Maximum1.7471965551376345
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:19.402878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.710629225
5-th percentile-1.506678379
Q1-0.89170672
median-0.02231267653
Q30.8640745729
95-th percentile1.5595689
Maximum1.747196555
Range3.45782578
Interquartile range (IQR)1.755781293

Descriptive statistics

Standard deviation0.9999999982
Coefficient of variation (CV)-5.541893275e+10
Kurtosis-1.247651317
Mean-1.804437488e-11
Median Absolute Deviation (MAD)0.8806081712
Skewness0.05048687397
Sum-1.804437488e-08
Variance0.9999999964
2020-08-25T00:40:19.677433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.73438048410.1%
 
-1.52869212610.1%
 
1.6029540310.1%
 
-0.0301730185710.1%
 
-1.69515490510.1%
 
1.15763366210.1%
 
0.16276986910.1%
 
-1.67325115210.1%
 
-0.678421139710.1%
 
1.06386947610.1%
 
1.56386816510.1%
 
1.43681991110.1%
 
-0.684271037610.1%
 
1.0298991210.1%
 
1.12370359910.1%
 
-1.30212140110.1%
 
0.686192870110.1%
 
-1.22789788210.1%
 
-1.47398793710.1%
 
-1.19664275610.1%
 
0.754570782210.1%
 
-1.43059635210.1%
 
-1.50913679610.1%
 
-1.25913417310.1%
 
-1.50131785910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.71062922510.1%
 
-1.70770764410.1%
 
-1.7048903710.1%
 
-1.69515490510.1%
 
-1.69390690310.1%
 
-1.68307614310.1%
 
-1.68277776210.1%
 
-1.68103575710.1%
 
-1.67529404210.1%
 
-1.67325115210.1%
 
ValueCountFrequency (%) 
1.74719655510.1%
 
1.74692261210.1%
 
1.74144375310.1%
 
1.7383521810.1%
 
1.73438048410.1%
 
1.73115265410.1%
 
1.72905397410.1%
 
1.71615123710.1%
 
1.71539235110.1%
 
1.71029770410.1%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.626329660415649e-10
Minimum-1.7157324552536009
Maximum1.699710249900818
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:19.794713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.715732455
5-th percentile-1.534369171
Q1-0.894780606
median-0.01758060046
Q30.8722117692
95-th percentile1.569380957
Maximum1.69971025
Range3.415442705
Interquartile range (IQR)1.766992375

Descriptive statistics

Standard deviation0.9999999981
Coefficient of variation (CV)3807595113
Kurtosis-1.211073444
Mean2.62632966e-10
Median Absolute Deviation (MAD)0.8865709901
Skewness0.0147044675
Sum2.62632966e-07
Variance0.9999999962
2020-08-25T00:40:19.899187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.67594206310.1%
 
1.13800549510.1%
 
0.244796663510.1%
 
0.183827042610.1%
 
-1.67053866410.1%
 
0.901595413710.1%
 
-1.09395062910.1%
 
0.0987152606210.1%
 
-1.21225273610.1%
 
1.28001773410.1%
 
0.288412660410.1%
 
-1.37239170110.1%
 
0.434010833510.1%
 
1.63392150410.1%
 
1.57550501810.1%
 
0.422194719310.1%
 
-1.04064822210.1%
 
1.59893071710.1%
 
-1.50127375110.1%
 
-0.625283658510.1%
 
-1.68485426910.1%
 
1.58277714310.1%
 
-0.619289398210.1%
 
0.904421210310.1%
 
-0.65054243810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.71573245510.1%
 
-1.71381604710.1%
 
-1.71051919510.1%
 
-1.70974981810.1%
 
-1.7059216510.1%
 
-1.70363676510.1%
 
-1.70358967810.1%
 
-1.7021284110.1%
 
-1.7008470310.1%
 
-1.69675850910.1%
 
ValueCountFrequency (%) 
1.6997102510.1%
 
1.69354939510.1%
 
1.6913760910.1%
 
1.68774163710.1%
 
1.68600702310.1%
 
1.68521094310.1%
 
1.68433916610.1%
 
1.68398010710.1%
 
1.68205261210.1%
 
1.6811906110.1%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.0646908776834606e-10
Minimum-1.7272379398345947
Maximum1.730407953262329
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:20.011894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.72723794
5-th percentile-1.554014063
Q1-0.8804571927
median-0.002619559877
Q30.8543565422
95-th percentile1.560678053
Maximum1.730407953
Range3.457645893
Interquartile range (IQR)1.734813735

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-9392397566
Kurtosis-1.216113681
Mean-1.064690878e-10
Median Absolute Deviation (MAD)0.8682433367
Skewness0.01926516386
Sum-1.064690878e-07
Variance1.000000002
2020-08-25T00:40:20.113879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.687497496610.1%
 
1.57382440610.1%
 
0.845035135710.1%
 
1.49741816510.1%
 
1.61743938910.1%
 
-0.321619182810.1%
 
-1.33462059510.1%
 
-1.01817798610.1%
 
1.53256154110.1%
 
0.807295262810.1%
 
1.47786724610.1%
 
0.956870317510.1%
 
0.890596568610.1%
 
0.958942055710.1%
 
1.28644907510.1%
 
0.581867814110.1%
 
1.42066073410.1%
 
1.25908386710.1%
 
-1.27470099910.1%
 
-0.727192640310.1%
 
0.987472474610.1%
 
0.641041874910.1%
 
-0.226540446310.1%
 
0.378240793910.1%
 
-1.5651609910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.7272379410.1%
 
-1.72612190210.1%
 
-1.71490061310.1%
 
-1.70967364310.1%
 
-1.70420312910.1%
 
-1.70380735410.1%
 
-1.7022610910.1%
 
-1.69532775910.1%
 
-1.69188165710.1%
 
-1.68978726910.1%
 
ValueCountFrequency (%) 
1.73040795310.1%
 
1.72584319110.1%
 
1.7234492310.1%
 
1.7204656610.1%
 
1.71370768510.1%
 
1.70854163210.1%
 
1.70624721110.1%
 
1.70395886910.1%
 
1.69803035310.1%
 
1.69779682210.1%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.3140961510060833e-09
Minimum-1.6951889991760254
Maximum1.7202317714691162
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:20.226404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.695188999
5-th percentile-1.559634775
Q1-0.8351323158
median-0.03826792911
Q30.9106476754
95-th percentile1.542967111
Maximum1.720231771
Range3.415420771
Interquartile range (IQR)1.745779991

Descriptive statistics

Standard deviation0.9999999999
Coefficient of variation (CV)-760979323.4
Kurtosis-1.210239286
Mean-1.314096151e-09
Median Absolute Deviation (MAD)0.8732705005
Skewness0.02424649562
Sum-1.314096151e-06
Variance0.9999999998
2020-08-25T00:40:20.327636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.763671100110.1%
 
1.00518262410.1%
 
-1.55533516410.1%
 
1.21225273610.1%
 
1.37240481410.1%
 
-0.973309814910.1%
 
-0.749630153210.1%
 
-0.162760287510.1%
 
-0.89820849910.1%
 
0.0809124857210.1%
 
-0.967441320410.1%
 
1.17822277510.1%
 
1.04815697710.1%
 
0.643218696110.1%
 
0.875639140610.1%
 
-1.38935065310.1%
 
-0.331355422710.1%
 
0.912744224110.1%
 
-1.29423010310.1%
 
0.934223532710.1%
 
1.24344325110.1%
 
1.08328175510.1%
 
-1.05983090410.1%
 
0.273128867110.1%
 
0.676651775810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.69518899910.1%
 
-1.69457757510.1%
 
-1.69231700910.1%
 
-1.6922490610.1%
 
-1.69038081210.1%
 
-1.6899770510.1%
 
-1.68649518510.1%
 
-1.6855688110.1%
 
-1.68447923710.1%
 
-1.6839194310.1%
 
ValueCountFrequency (%) 
1.72023177110.1%
 
1.71688425510.1%
 
1.7054754510.1%
 
1.70246505710.1%
 
1.7007399810.1%
 
1.69914889310.1%
 
1.69449949310.1%
 
1.69146084810.1%
 
1.68831145810.1%
 
1.68292784710.1%
 

target
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.669441401958466e-10
Minimum-2.563552141189575
Maximum4.102142333984375
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:40:20.443825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.563552141
5-th percentile-1.850279075
Q1-0.6148845851
median0.1451399848
Q30.6587753743
95-th percentile1.3758816
Maximum4.102142334
Range6.665694475
Interquartile range (IQR)1.273659959

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)1303875926
Kurtosis0.2581949736
Mean7.669441402e-10
Median Absolute Deviation (MAD)0.6040655375
Skewness-0.1728235441
Sum7.669441402e-07
Variance1.000000002
2020-08-25T00:40:20.547325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.34765553510.1%
 
-1.31280612910.1%
 
-0.034345902510.1%
 
0.0582715682710.1%
 
0.2945698510.1%
 
1.23958659210.1%
 
-0.330429196410.1%
 
-0.106288783310.1%
 
1.65974962710.1%
 
-1.31389439110.1%
 
0.488628566310.1%
 
1.22794985810.1%
 
-0.357767492510.1%
 
-0.548839628710.1%
 
-0.619823157810.1%
 
0.965523421810.1%
 
1.22916734210.1%
 
-0.278645068410.1%
 
-1.45838427510.1%
 
0.664737522610.1%
 
-0.820983469510.1%
 
-1.98303449210.1%
 
-1.06774330110.1%
 
2.99520182610.1%
 
0.358274787710.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.56355214110.1%
 
-2.4330072410.1%
 
-2.33355212210.1%
 
-2.32539391510.1%
 
-2.3121969710.1%
 
-2.29562807110.1%
 
-2.29190468810.1%
 
-2.27044439310.1%
 
-2.24550414110.1%
 
-2.23206949210.1%
 
ValueCountFrequency (%) 
4.10214233410.1%
 
3.43669891410.1%
 
3.1801819810.1%
 
3.14205288910.1%
 
2.99520182610.1%
 
2.98918008810.1%
 
2.93411517110.1%
 
2.75330781910.1%
 
2.68528699910.1%
 
2.6374635710.1%
 

Interactions

2020-08-25T00:39:57.807707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:57.950403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:58.106740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:58.250849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:58.400182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:58.545713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:58.702791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:58.854416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:59.004763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:59.156791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:59.307364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:59.458687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:59.612660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:59.772613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:39:59.928472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:00.090091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:00.245844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:00.404507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:00.567581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:00.728325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:00.904809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:01.066967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:01.227168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:01.542488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:01.699418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:01.847594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:02.001327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:02.154082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:02.307444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:02.462182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:02.619013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:02.774189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:02.928433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:03.082724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:03.233790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:03.395169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:03.552930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:03.715242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:03.871086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:04.030092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:04.192348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:04.353555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:04.515344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:04.676849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:04.836073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:04.982318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:05.138407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:05.288738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:05.443371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:05.596182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:05.752610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:05.914058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:06.069413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:06.390506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:06.544879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:06.701047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:06.850827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:07.007714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:07.161899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:07.324110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:07.478295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:07.634595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:07.795780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:07.955749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:08.114965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:08.274304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:08.432753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:08.583864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:08.750140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:08.905097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:09.067858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:09.225887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:09.383579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:09.544033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:09.705851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:09.869055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:10.029924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:10.192291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:10.349734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:10.512698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:10.669076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:10.834783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:11.152005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:11.311485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:11.472083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:11.633912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:11.799389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:11.968350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:12.129362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:12.285016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:12.446384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:12.598377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:12.763413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:12.918192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:13.078587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:13.239937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:13.400383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:13.559944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:13.734261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:13.903036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:14.057166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:14.220831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:14.374916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:14.535568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:14.690736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:14.851140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:15.015450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:15.174629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:15.335349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:15.494131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:15.656055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:15.978009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:16.138539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:16.293036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:16.452090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:16.606066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:16.762731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:16.927478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:17.087821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:17.249105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:17.412449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:40:20.674241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:40:20.907236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:40:21.134626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:40:21.358558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:40:17.677680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:40:17.948694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
00.9034451.364080-0.314574-0.601459-0.2161062.0043041.539346-0.218686-0.0048561.558926-1.051093
1-0.403362-1.133148-0.653747-0.762931-0.441265-0.591847-0.166339-0.9491480.387953-0.8232760.154259
2-0.771931-0.766264-0.672213-0.071760-0.6597920.669554-0.0707931.521835-0.227403-1.1521950.597957
3-0.0268620.066554-0.596093-0.638973-0.3099481.9081100.5312510.8855710.4975651.5421180.354699
40.542034-0.1546220.6012881.0316470.405714-0.444295-0.4260000.805068-1.645485-0.2029520.467010
50.6848950.8890521.1504540.8233181.007051-0.178330-0.2894990.546447-0.2114731.144251-1.313581
6-1.206384-1.083423-0.509904-1.391226-0.6855181.1282030.9681440.0426040.7053680.141496-0.524030
70.568481-0.174016-0.807298-0.886063-0.665081-0.401443-0.4526090.141335-0.742963-1.0076400.074067
81.6253731.226461-0.994304-1.540964-0.5843940.7668961.0276150.4700130.4661270.042073-0.010032
9-0.628520-0.7794370.2218760.224829-0.300175-0.6686411.607391-1.472267-1.594331-1.6794990.762208

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
9900.9054951.0866480.209652-1.3176720.265977-0.825949-0.415831-1.116775-0.810537-0.217980-2.054256
991-0.0233980.043550-0.152341-0.095392-0.598324-1.712183-1.3375500.193309-0.528305-0.8960240.515064
9921.3170921.3985980.577272-0.2664751.008199-0.021628-0.6919541.521291-0.5089061.6883110.394862
9931.3531251.557266-0.737865-1.361454-0.3533640.0129770.8485980.9365060.629787-1.5996880.252701
994-1.652026-1.609342-1.2983181.235984-0.655891-0.1319010.890974-0.007749-0.754718-1.6600820.428960
995-2.020360-1.412216-1.2040681.675094-0.8236760.6572381.075431-1.170320-0.0502500.2702530.859208
9961.3434910.914436-0.806780-1.378847-0.986458-0.044701-0.0068290.3437451.175569-0.834112-1.806569
997-0.516426-0.412374-0.633792-0.723687-0.7988110.5589620.127094-0.757338-1.2747011.6414140.888657
998-0.222495-0.979397-0.3957021.217792-0.4063251.1594531.731153-1.279248-0.6415861.0170380.935562
999-0.804155-0.541875-0.1766890.502820-0.1562101.1780140.6676610.080943-1.4298760.5248741.097957